Conventionally, a technique of estimating the state of a mobile body using a particle filter is known. The particle filter is used in a method of approximating a probability distribution of the state of the mobile body based on a set of particles representing a given state of the mobile body.
According to the technique of estimating the state of a mobile body using the particle filter, a particle group representing the current state of the mobile body is estimated from a particle group that represents a past state of the mobile body through simulation. Based on the likelihood (level of likelihood) of each particle in the estimated particle group representing the current state of the mobile body, re-sampling of the particle group is carried out. In re-sampling of the particle group, particles making up the particle group representing the current state of the mobile body are re-selected from among the particle group estimated by simulation, and the current state of the mobile body is re-estimated.
Several examples of re-sampling techniques may be cited. According to one technique, the position of the mobile body is estimated using a particle filter and is corrected by the mobile body based on a measurement from a laser radar of the mobile body (see, e.g., Japanese Laid-Open Patent Publication No. 2009-223504). According to another technique the position of the mobile body is estimated using a particle filter and is corrected by the mobile body based on an ID signal that is received from and unique to an infrared irradiating device (see, e.g., Japanese Laid-Open Patent Publication No. 2009-176031).
According to still another technique, the number of particles used for estimating the state of the mobile body is dynamically changed (see, e.g., Japanese Laid-Open Patent Publication No. 2010-224755). According to another technique, a grid map is generated from a geometric map, whereby a Voronoi graph is created from the generated grid map, using distances corresponding to the uncertainty of the position/orientation of an object, and on the generated Voronoi graph, a path is searched for based on a probability of collision with the object and path length (see, e.g., Japanese Laid-Open Patent Publication No. 2005-32196). According to another technique, the position of the mobile body is estimated by the mobile body by integrating information from multiple sensors (see, e.g., Japanese Laid-Open Patent Publication No. 2008-33696).
According to the above conventional techniques, however, particles representing the true state of the mobile body may not be included in the particle group representing the current state of the mobile body estimated by simulation using the particle filter. Such a case happens, for example, when calculation errors made at each round of the simulation grow in number as the simulation is repeated. In this case, even if a reasonable particle group representing the current state of the mobile body is re-selected from among the estimated particle group indicating the current state of the mobile body through re-sampling, the re-selected particle group may not include the particles indicating the true state of the mobile body. As a result, according to the conventional techniques, the position of the mobile body may be erroneously estimated from the state of the mobile body indicated by the re-selected particle group, whereby the mobile body enters an area that has been set as an area that the mobile body should not enter.